Relational and graph-structured data has become ubiquitous in many fields of application such as social network analysis, bioinformatics, artificial intelligence, or factory processing. Therefore, learning from large-scale relational data is an increasingly important field.
As a consequence of increasing volume and complexity of data, scalability and modeling power become crucial for learning machine algorithms dealing with the large-scale relational data. Approaches involve for instance logical representations of the model (e.g., Inductive Logic Programming or Markov Logic Networks) or include a set of latent variables (e.g., the Infinite Hidden Relational Model or the Infinite Relational Model).
Latent variable models allow deducing unknown relationships hidden in the data. An important approach is tensor factorization, which is a generalization of matrix factorization to higher-order data. In the past years, tensor factorization methods have become popular for learning from multi-relational data.